Hostname: page-component-76fb5796d-skm99 Total loading time: 0 Render date: 2024-04-25T10:11:32.363Z Has data issue: false hasContentIssue false
  • English
  • Français

Learning inexpensive parametric design models using an augmented genetic programming technique

Published online by Cambridge University Press:  10 February 2006

PETER C. MATTHEWS ,
DAVID W.F. STANDINGFORD ,
CARREN M.E. HOLDEN  and
KEN M. WALLACE
PETER C. MATTHEWS
Affiliation:
School of Engineering, University of Durham, Durham, United Kingdom
DAVID W.F. STANDINGFORD
Affiliation:
BAE Systems, Advanced Technology Centre, Filton, Bristol, United Kingdom
CARREN M.E. HOLDEN
Affiliation:
Aerodynamic Methods and Tools, Airbus UK, Filton, Bristol, United Kingdom
KEN M. WALLACE
Affiliation:
Engineering Design Centre, Engineering Department, University of Cambridge, Cambridge, United Kingdom
Get access Rights & Permissions [Opens in a new window]

Abstract

Previous applications of genetic programming (GP) have been restricted to searching for algebraic approximations mapping the design parameters (e.g., geometrical parameters) to a single design objective (e.g., weight). In addition, these algebraic expressions tend to be highly complex. By adding a simple extension to the GP technique, a powerful design data analysis tool is developed. This paper significantly extends the analysis capabilities of GP by searching for multiple simple models within a single population by splitting the population into multiple islands according to the design variables used by individual members. Where members from different islands “cooperate,” simple design models can be extracted from this cooperation. This relatively simple extension to GP is shown to have powerful implications to extracting design models that can be readily interpreted and exploited by human designers. The full analysis method, GP heuristics extraction method, is described and illustrated by means of a design case study.

Keywords

Data MiningDesign Model InductionGenetic ProgrammingKnowledge ElicitationMetamodels
Type
Research Article
Information
AI EDAM , Volume 20 , Issue 1 , February 2006 , pp. 1 - 18
Copyright
2006 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Ahmed, S. (2001). Understanding the use and reuse of experience in engineering design. PhD Thesis. University of Cambridge, Engineering Department.
Ahmed, S. & Wallace, K.M. (2004). Understanding the knowledge needs of novice designers in the aerospace industry. Design Studies 25(2), 155–173.CrossRefGoogle Scholar
Andre, D. & Koza, J. (1996). A parallel implementation of genetic programming that achieves super-linear performance. Proc. Int. Conf. Parallel and Distributed Processing Techniques and Applications (Hamid, R., Ed.), Vol. 3, pp. 1163–1174, Athens, GA.
Andrews, R., Diederich, J., & Tickle, A.B. (1995). Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-Based Systems 8(6), 373–389.CrossRefGoogle Scholar
Arafat, G.H., Goodman, B., & Arciszewski, T. (1993). Ramzes: a knowledge-based system for structural concepts evaluation. Computing Systems in Engineering 4(2–3), 211–221.CrossRefGoogle Scholar
Arciszewski, T. (1997). Engineering semantic evaluation of decision rules. Journal of Intelligent and Fuzzy Systems 5(3), 285–295.Google Scholar
Arciszewski, T. & Ziarko, W. (1992). Knowledge Acquisition in Civil Engineering, pp. 50–68. New York: American Society of Civil Engineers.
Belding, T.C. (1995). The distributed genetic algorithm revisited. Proc. Sixth Int. Conf. Genetic Algorithms (Eshelman, L., Ed.), pp. 114–121. San Francisco, CA: Morgan Kaufmann.
Blessing, L.T.M. (1994). A process-based approach to computer-supported engineering design. PhD Thesis. University of Twente.
Clarke, S.M., Griebsch, J.H., & Simpson, T.W. (2003). Analysis of support vector regression for approximation of complex engineering analyses. Proc. ASME 2003 Design Engineering Technical Conf., Paper No. DETC2003/DEC48759, Chicago.CrossRef
Cohoon, J.P., Hegde, S.U., Martin, W.N., & Richards, D. (1987). Punctuated equilibria: a parallel genetic algorithm. Proc. Second Int. Conf. Genetic Algorithms and Their Application, pp. 148–154. Hillsdale, NJ: Erlbaum.
Corbett-Clark, T.A. (1998). Explanation from neural networks. PhD Thesis. Oxford University, Department of Engineering Science.
Duch, W., Setiono, R., & Żurada, J.M. (2004). Computational intelligence methods for rule-based data understanding. Proceedings of the IEEE 92(5), 771–805.CrossRefGoogle Scholar
Eastman, C.M., Bond, A.H., & Chase, S.C. (1991). A formal approach for product model information. Research in Engineering Design 2(2), 65–80.CrossRefGoogle Scholar
Gen, M. & Cheng, R. (2000). Genetic Algorithms and Engineering Optimization. New York: Wiley.
Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA: Addison–Wesley.
Green, G. (1997). Modelling concept design evaluation. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 11(3), 211–217.CrossRefGoogle Scholar
Heckerman, D. (1999). A tutorial on learning with Bayesian networks. In Learning in Graphical Models (Jordan, M.I., Ed.), pp. 301–354. Cambridge, MA: MIT Press.
Hong, T.P., Wang, T.T., Wang, S.L., & Chien, B.C. (2000). Learning a coverage set of maximally general fuzzy rules by rough sets. Expert Systems with Applications 19(2), 97–103.CrossRefGoogle Scholar
Huang, S.H. & Xing, H. (2002). Extract intelligible and concise fuzzy rules from neural networks. Fuzzy Sets and Systems 132(2), 233–243.CrossRefGoogle Scholar
Hwang, S.Y. & Yang, W.S. (2002). On the discovery of process models from their instances. Decision Support Systems 34(1), 41–57.CrossRefGoogle Scholar
Ishino, Y. & Jin, Y. (2002). Estimate design intent: a multiple genetic programming and multivariate analysis approach. Advanced Engineering Informatics 16(2), 107–125.CrossRefGoogle Scholar
Jenkins, W.M. (1996). A genetic algorithm for structural design optimization. Proc. NATO Advanced Science Institutes. Series F: Computer and Systems Sciences. Emergent Computing Methods in Engineering Design: Applications of Genetic Algorithms and Neural Networks, Vol. 149, pp. 30–52. Berlin: Springer–Verlag.CrossRef
Johansson, U., Niklasson, L., & König, R. (2004). Accuracy vs. comprehensibility in data mining models. Proc. Seventh Int. Conf. Information Fusion (Svensson, P. & Schubert, J., Eds.), Vol. 1, pp. 295–300, Mountain View, CA.
Kaufman, L. & Rousseeuw, P.J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis, Probability and Mathematical Statistics. New York: Wiley.CrossRef
Koza, J.R. (1992). Genetic Programing: On the Programming of Computers by Means of Natural Selection, Complex Adaptive Systems. Cambridge, MA: MIT Press.
Koza, J.R., Bennet, F. H., III, Andre, D., & Keane, M.A. (1999). Genetic Programming III: Darwinian Invention and Problem Solving. San Francisco, CA: Morgan Kaufmann.
Kusiak, A. (2001). Rough set theory: a data mining tool for semiconductor manufacturing. IEEE Transactions on Electronic Packaging Manufacturing 24(1), 44–50.CrossRefGoogle Scholar
Lakshminarayanan, S., Fujii, H., Grosman, B., Dassau, E., & Lewin, D.R. (2000). New product design via analysis of historical databases. Computers and Chemical Engineering 24(2), 671–676.CrossRefGoogle Scholar
Langley, P., Simon, H.A., Bradshaw, G.L., & Żytkow, J.M. (1987). Scientific Discovery: Computational Explorations of the Creative Processes. Cambridge, MA: MIT Press.
Lawrence, S., Tsoi, A.C., & Back, A.D. (1996). Function approximation with neural networks and local methods: Bias, variance and smoothness. Proc. Australian Conf. Neural Networks (Bartlett, P., Burkitt, A. & Williamson, R., Eds.), pp. 16–21, Australian National University.
Leake, D.B., Birnbaum, L., Hammond, K., Marlow, C., & Yang, H. (1999). Case-Based Reasoning Research and Development 1999. Lecture Notes in Artificial Intelligence, Vol. 1650, pp. 482–496. Berlin: Springer–Verlag.
Maire, F. (1999). Rule-extraction by backpropagation of polyhedra. Neural Networks 12(4–5), 717–725.CrossRefGoogle Scholar
Malmqvist, J. & Schachinger, P. (1997). Towards an implementation of the chromosome model—Focusing the design specification. Proc. 11th Int. Conf. Engineering Design (Riitahuhta, A., Ed.), Vol. 3, pp. 203–212, Tampere University of Technology.
Matthews, P.C. (2002). The application of self organizing maps in conceptual design. PhD Thesis. University of Cambridge, Engineering Department.
Michalski, R.S. & Kaufman, K.A. (1997). Data mining and knowledge discovery: a review of issues and a multistrategy approach. In Machine Learning and Data Mining: Methods and Applications (Michalski, R.S., Bratko, I. & Kubat, M., Eds.), pp. 71–112. Chichester: Wiley.
Michalski, R.S. & Tecuci, G., Eds. (1994). Machine Learning: A Multistrategy Approach, Vol. IV. San Francisco, CA: Morgan Kaufmann.
Mitchell, T.M. (1997). Machine Learning. New York: McGraw–Hill.
Modesitt, K.L. (1992). Basic principles and techniques in knowledge acquisition. In Knowledge Acquisition in Civil Engineering, pp. 11–49. New York: American Society of Civil Engineers.
Nair, P.B., Choudhury, A., & Keane, A.J. (2002). Some greedy learning algorithms for sparse regression and classification with Mercer kernels. Journal of Machine Learning Research, 3, 781–801.Google Scholar
Popovic, V. (2004). Expertise development in a product design—Strategic and domain-specific knowledge connections. Design Studies 25(5), 527–545.CrossRefGoogle Scholar
Potter, M.A. (1997). The design and analysis of a computational model of cooperative coevolution. PhD Thesis. George Mason University.
Potter, M.A. & De Jong, K.A. (2000). Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evolutionary Computation 8(1), 1–29.CrossRefGoogle Scholar
Potter, M.A., De Jong, K.A., & Grefenstette, J.J. (1995). A coevolutionary approach to learning sequential decision rules. In Proc. Sixth Int. Conf. Genetic Algorithms (Eshelman, L., Ed.), pp. 366–372. San Francisco, CA: Morgan Kaufmann.
Reich, Y. & Barai, S.V. (1999). Evaluating machine learning models for engineering problems. Artificial Intelligence in Engineering 13(2), 257–272.CrossRefGoogle Scholar
Rosenman, M. (2000). Case-based evolutionary design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 14(1), 17–29.CrossRefGoogle Scholar
Shadbolt, N.R. & Milton, N. (1999). From knowledge engineering to knowledge management. British Journal of Management 10(4), 309–322.CrossRefGoogle Scholar
Siddall, J.N. (1986). Probabilistic modelling in design. ASME Journal of Mechanisms, Transmissions, and Automation in Design 108(3), 330–335.CrossRefGoogle Scholar
Simpson, T.W., Peplinski, J.D., Koch, P.N., & Allen, J.K. (2001). Metamodels for computer-based engineering design: survey and recommendations. Engineering with Computers 17(2), 129–150.CrossRefGoogle Scholar
Smith, R.P. & Morrow, J. (1999). Product development process modeling. Design Studies 20(3), 237–261.CrossRefGoogle Scholar
Tanese, R. (1989). Distributed genetic algorithms for function optimization. PhD Thesis. University of Michigan.
Thornton, A.C. (1996). The use of constraint-based design knowledge to improve the search for feasible designs. Engineering Applications of Artificial Intelligence 9(4), 393–402.CrossRefGoogle Scholar
Wegkamp, M. (2003). Model selection in nonparametric regression. The Annals of Statistics 31(1), 252–273.CrossRefGoogle Scholar
Wiegand, R.P., Liles, W.C., & De Jong, K.A. (2001). An empirical analysis of collaboration methods in cooperative coevolutionary algorithms. Proc. Genetic and Evolutionary Computation Conf. (GECCO2001) (Spector, L., Goodman, E.D., Wu, A., Langdon, W.B., Voigt, H.-M., Gen, M., Sen, S., Dorigo, M., Pezeshk, S., Garzon, M.H. & Burke, E., Eds.), pp. 1235–1242. San Francisco, CA: Morgan Kaufmann.
Żytkow, J.M. (1999). The melting pot of automated discovery: principles for a new science. In Discovery Science: Second Int. Conf. Lecture Notes in Artificial Intelligence, Vol. 1721, pp. 1–12. Berlin: Springer–Verlag.
Żytkow, J.M. (2000). Automated discovery: a fusion of multidisciplinary principles. In Advances in Artificial Intelligence. Lecture Notes in Artificial Intelligence, Vol. 1822, pp. 443–448. Berlin: Springer–Verlag.